A neural network approach to system performance analysis

被引:0
作者
Gruen, R [1 ]
Kubota, T [1 ]
机构
[1] VC3 Inc, Columbia, SC USA
来源
IEEE SOUTHEASTCON 2002: PROCEEDINGS | 2002年
关键词
kohonen neural netowork; SOFM; system performance; data analysis;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Neural networks are used in a wide variety of situations to solve complex problems. Some of the categories for which neural networks are used are: prediction software, classification algorithms, data association environments, data conceptualization environments, and data filtering problems. This work described in this paper implements a neural network that spans both the prediction and data association problems. The neural network approach to system performance analysis takes performance data from computer systems and uses a Kohonen based neural network to analyze the performance data and attempts to find bottlenecks in the computer system. The data performance analysis results are present as line graphs that can be interpreted by computer experts to determine bottlenecks within the computer system and can intelligently suggest upgrades to improve any subsystem that suffers from poor performance. The aim of this work is to provide a "proof of concept" for use in IT assessments but can also be applied to any situation involving computer performance analysis.
引用
收藏
页码:349 / 354
页数:6
相关论文
共 50 条
[21]   A survey of sentiment analysis methods based on graph neural network [J].
Abedi Rad, Razieh ;
Yamaghani, Mohammad Reza ;
Nourbakhsh, Azamossadat .
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, 2025, 19 (03) :389-417
[22]   Surface tension prediction of pure organic components: An artificial neural network approach [J].
Queiroz, Lucas Silva ;
Bueno, Vinicius Ferreira da Silva ;
dos Santos, Hyago Braga ;
Gatti, Larissa Maria ;
Ahon, Victor Rolando Ruiz ;
Assenheimer, Troner .
FUEL, 2025, 380
[23]   A Neural Network to Estimate Isolated Performance from Multi-Program Execution [J].
Lurbe, Manel ;
Feliu, Josue ;
Petit, Salvador ;
Gomez, Maria E. ;
Sahuquillo, Julio .
30TH EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND NETWORK-BASED PROCESSING (PDP 2022), 2022, :63-66
[24]   Comparison of Heat Demand Prediction Using Wavelet Analysis and Neural Network for a District Heating Network [J].
Kovac, Szabolcs ;
Micha'conok, German ;
Halenar, Igor ;
Vazan, Pavel .
ENERGIES, 2021, 14 (06)
[25]   An Approach for Fuzzy Modeling based on Self-Organizing Feature Maps Neural Network [J].
Chen, Ching-Yi ;
Chiang, Jen-Shiun ;
Chen, Kuang-Yuan ;
Liu, Ta-Kang ;
Wong, Ching-Chang .
APPLIED MATHEMATICS & INFORMATION SCIENCES, 2014, 8 (03) :1207-1215
[26]   The Performance of Graph Neural Network in Detecting Fake News from Social Media Feeds [J].
Shovon, Iftekharul Islam ;
Shin, Seokjoo .
2023 INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING, ICOIN, 2023, :560-564
[27]   Enhancing the performance of the neural network model for the EMG regression case using Hadamard product [J].
Kim, Won-Joong ;
Kim, Inwoo ;
Lee, Soo-Hong .
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2024, 38 (07) :3607-3613
[28]   Enhancing the performance of the neural network model for the EMG regression case using Hadamard product [J].
Kim, Won-Joong ;
Kim, Inwoo ;
Lee, Soo-Hong .
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2024, 38 (07) :3607-3613
[29]   Performance Analysis and Simulation of Turbo Coding System [J].
Liu, Zeng ;
Chen, Jin ;
Ji, Maolin ;
Tong, Ying ;
Wang, Lujia ;
Liu, Hengxin .
COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS, 2018, 423 :451-457
[30]   Error performance analysis of artificial neural networks applied to Rutherford backscattering [J].
Vieira, A ;
Barradas, NP ;
Jeynes, C .
SURFACE AND INTERFACE ANALYSIS, 2001, 31 (01) :35-38